diff --git a/lightrag/kg/networkx_impl.py b/lightrag/kg/networkx_impl.py
index 581a4187..c88d1c59 100644
--- a/lightrag/kg/networkx_impl.py
+++ b/lightrag/kg/networkx_impl.py
@@ -214,12 +214,6 @@ class NetworkXStorage(BaseGraphStorage):
         """
         labels = set()
         for node in self._graph.nodes():
-            # node_data = dict(self._graph.nodes[node])
-            # if "entity_type" in node_data:
-            #     if isinstance(node_data["entity_type"], list):
-            #         labels.update(node_data["entity_type"])
-            #     else:
-            #         labels.add(node_data["entity_type"])
             labels.add(str(node))  # Add node id as a label
 
         # Return sorted list
diff --git a/lightrag/llm/hf.py b/lightrag/llm/hf.py
index d678c611..fb5208b0 100644
--- a/lightrag/llm/hf.py
+++ b/lightrag/llm/hf.py
@@ -139,11 +139,14 @@ async def hf_model_complete(
 
 async def hf_embed(texts: list[str], tokenizer, embed_model) -> np.ndarray:
     device = next(embed_model.parameters()).device
-    input_ids = tokenizer(
+    encoded_texts = tokenizer(
         texts, return_tensors="pt", padding=True, truncation=True
-    ).input_ids.to(device)
+    ).to(device)
     with torch.no_grad():
-        outputs = embed_model(input_ids)
+        outputs = embed_model(
+            input_ids=encoded_texts["input_ids"],
+            attention_mask=encoded_texts["attention_mask"],
+        )
         embeddings = outputs.last_hidden_state.mean(dim=1)
     if embeddings.dtype == torch.bfloat16:
         return embeddings.detach().to(torch.float32).cpu().numpy()